Why bioimage informatics matters

Myers, Gene

doi:10.1038/nmeth.2024

Cited by 70 publications

(59 citation statements)

References 12 publications

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“…In fact, a shift of this weighting towards bioimage informatics within quantitative biology is to be expected more and more in the future. In this context, both the computational instrumentation and, moreover, the ability to apply image and data analysis will define the future perspectives of scientists in the life sciences (Carpenter et al, 2012; Eliceiri et al, 2012; Myers, 2012). …”

Section: Data Analysis and Data Managementmentioning

confidence: 99%

Advanced light microscopy core facilities: Balancing service, science and career

Ferrando‐May

Hartmann

Reymann

et al. 2016

Microsc. Res. Tech.

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Core Facilities (CF) for advanced light microscopy (ALM) have become indispensable support units for research in the life sciences. Their organizational structure and technical characteristics are quite diverse, although the tasks they pursue and the services they offer are similar. Therefore, throughout Europe, scientists from ALM‐CFs are forming networks to promote interactions and discuss best practice models. Here, we present recommendations for ALM‐CF operations elaborated by the workgroups of the German network of ALM‐CFs, German Bio‐Imaging (GerBI). We address technical aspects of CF planning and instrument maintainance, give advice on the organization and management of an ALM‐CF, propose a scheme for the training of CF users, and provide an overview of current resources for image processing and analysis. Further, we elaborate on the new challenges and opportunities for professional development and careers created by CFs. While some information specifically refers to the German academic system, most of the content of this article is of general interest for CFs in the life sciences. Microsc. Res. Tech. 79:463–479, 2016. © 2016 THE AUTHORS MICROSCOPY RESEARCH AND TECHNIQUE PUBLISHED BY WILEY PERIODICALS, INC.

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Section: Data Analysis and Data Managementmentioning

confidence: 99%

Advanced light microscopy core facilities: Balancing service, science and career

Ferrando‐May

Hartmann

Reymann

et al. 2016

Microsc. Res. Tech.

View full text Add to dashboard Cite

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“…This aspect probably will remain the major bottleneck in deriving meaningful data from multiparametric long-term continuous single-cell time-lapse microscopy for some time (Cardona and Tomancak, 2012;Carpenter et al, 2012;Myers, 2012).…”

Section: Image Analysis Quantification and Displaymentioning

confidence: 99%

Probing cellular processes by long-term live imaging – historic problems and current solutions

Coutu

Schroeder

2013

Journal of Cell Science

104

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SummaryLiving organisms, tissues, cells and molecules are highly dynamic. The importance of their continuous and long-term observation has been recognized for over a century but has been limited by technological hurdles. Improvements in imaging technologies, genetics, protein engineering and data analysis have more recently allowed us to answer long-standing questions in biology using quantitative continuous long-term imaging. This requires a multidisciplinary collaboration between scientists of various backgrounds: biologists asking relevant questions, imaging specialists and engineers developing hardware, and informaticians and mathematicians developing software for data acquisition, analysis and computational modeling. Despite recent improvements, there are still obstacles to be addressed before this technology can achieve its full potential. This Commentary aims at providing an overview of currently available technologies for quantitative continuous long-term single-cell imaging, their limitations and what is required to bring this field to the next level. We provide an historical perspective on the development of this technology and discuss key issues in time-lapse imaging: keeping cells alive, using labels, reporters and biosensors, and hardware and software requirements. We highlight crucial and often nonobvious problems for researchers venturing into the field and hope to inspire experts in the field and from related disciplines to contribute to future solutions.

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“…Bioimage informatics methods offer powerful solutions for specific image analysis tasks, such as object detection, motion analysis or measurements of morphometric features (Danuser, 2011;Murphy, 2011;Eliceiri et al, 2012;Myers, 2012). Most image analysis algorithms, however, have been developed for specific biological assays.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in cell biology – teaching computers to recognize phenotypes

Sommer

Gerlich

2013

Journal of Cell Science

283

237

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SummaryRecent advances in microscope automation provide new opportunities for high-throughput cell biology, such as image-based screening. High-complex image analysis tasks often make the implementation of static and predefined processing rules a cumbersome effort. Machine-learning methods, instead, seek to use intrinsic data structure, as well as the expert annotations of biologists to infer models that can be used to solve versatile data analysis tasks. Here, we explain how machine-learning methods work and what needs to be considered for their successful application in cell biology. We outline how microscopy images can be converted into a data representation suitable for machine learning, and then introduce various state-of-the-art machine-learning algorithms, highlighting recent applications in image-based screening. Our Commentary aims to provide the biologist with a guide to the application of machine learning to microscopy assays and we therefore include extensive discussion on how to optimize experimental workflow as well as the data analysis pipeline.

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Why bioimage informatics matters

Abstract: Driven by the importance of spatial and physical factors in cellular processes and the size and complexity of modern image data, computational analysis of biological imagery has become a vital emerging sub-discipline of bioinformatics and computer vision.

Cited by 70 publications

References 12 publications

Advanced light microscopy core facilities: Balancing service, science and career

Advanced light microscopy core facilities: Balancing service, science and career

Probing cellular processes by long-term live imaging – historic problems and current solutions

Machine learning in cell biology – teaching computers to recognize phenotypes

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